Gradient analyses operating on connectivity data were applied to diffusion MRI tractography data in specific brain regions 12, 13 as well as neocortical, hippocampal, and cerebellar resting-state functional MRI connectivity maps 11, 14, 15, 16, 17, 18, 19, 20. Such characterizations of cortical areas by their placement in the broader cortical hierarchy has provided a foundation for understanding functions that emerge through cortical interactions.Īlthough much of the more recent work linking measures of neural processing (for example from functional magnetic resonance imaging, MRI) to cognition has focused on identifying discrete regions and modules and their specific functional roles 10, recent conceptual and methodological developments have provided the data and methods that allow macroscale brain features mapped to low dimensional manifold representations, also described as gradients 11. In parallel, neuroanatomists have established spatial trends that span across cortical areas both in terms of their histological properties, and connectivity patterns 6, 7, 8, 9. On the one hand, studies have demarcated structurally homogeneous areas with specific connectivity profiles, and ultimately distinct functional roles 1, 2, 3, 4, 5. Over the last century, neuroanatomical studies in humans and non-human animals have highlighted two complementary features of neural organization. Validation experiments demonstrate the usage and consistency of our tools for the analysis of functional and microstructural gradients across different spatial scales. Our toolbox furthermore allows for controlled association studies between gradients with other brain-level features, adjusted with respect to null models that account for spatial autocorrelation. Here, we present BrainSpace, a Python/Matlab toolbox for (i) the identification of gradients, (ii) their alignment, and (iii) their visualization.
By quantifying topographic principles of macroscale organization, cortical gradients lend an analytical framework to study structural and functional brain organization across species, throughout development and aging, and its perturbations in disease. Recent work established that macroscale brain organization and function can be described in a compact manner with multivariate machine learning approaches that identify manifolds often described as cortical gradients. Understanding how cognitive functions emerge from brain structure depends on quantifying how discrete regions are integrated within the broader cortical landscape.
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